sklearn PipelinesChaining a StandardScaler with a KNeighborsClassifier model.
sklearn’s ColumnTransformerhandle_unknown = "ignore" of OneHotEncoderhandle_unknown='ignore' with OneHotEncoder to safely ignore unseen categories during transform.drop="if_binary" argument of OneHotEncodersklearn CountVectorizerUse scikit-learn’s CountVectorizer to encode text data
CountVectorizer: Transforms text into a matrix of token counts
Important parameters: - max_df, min_df: Control document frequency thresholds. - ngram_range: Defines the range of n-grams to be extracted.
iClicker cloud join link: https://join.iclicker.com/VYFJ
Select all of the following statements which are TRUE.
ColumnTransformer object to cross_validate.fit_transform on a ColumnTransformer object, you get a numpy ndarray.iClicker cloud join link: https://join.iclicker.com/VYFJ
Select all of the following statements which are TRUE.
handle_unknown="ignore" would treat all unknown categories equally.max_features hyperparameter of CountVectorizer the training score is likely to go up.CountVectorizer. If you encounter a word in the validation or the test split that’s not available in the training data, we’ll get an error.cross_validate, each fold might have slightly different number of features (columns) in the fold.